For this thesis, different algorithms were created to solve the problem of Jop Shop Scheduling with a number of constraints. More specifically, the setting of a (simplified) car workshop was used: the algorithms had to assign all tasks of one day to the mechanics, taking into account minimum and maximum finish times of tasks and mechanics, the use of bridges, qualifications and the delivery and...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

Many learning tasks involve searching through a discrete space of performance elements, seeking an element whose future utility is expected to be high. As the task of nding the global optimum is often intractable, many practical learning systems use simple forms of hill-climbing to nd a locally optimal element. However, hill-climbing can be complicated by the fact that the utility value of a pe...

Enforced hill-climbing is an effective deterministic hillclimbing technique that deals with local optima using breadth-first search (a process called “basin flooding”). We propose and evaluate a stochastic generalization of enforced hill-climbing for online use in goal-oriented probabilistic planning problems. We assume a provided heuristic function estimating expected cost to the goal with fla...

Combinations of metaheuristics to solve highly constrained combinatorial optimisation problems have recently become a promising research area. Some combinations emerge from integrating evolutionary algorithms and local based algorithms to strike the right balance between global exploration of the search region and local exploitation of promising regions. This study presents hybridization betwee...

The Traveling Tournament Problem (TTP) schedules a double roundrobin tournament to minimize the total distance traveled by competing teams. It involves issues of feasibility and optimality and is challenging for both constraint programming and integer programming. This paper considers the TTP proposed in [4],[[12]. We divide the search space into two subspaces. Simulated Annealing (SA) is used ...

This paper presents a convergence analysis for the problem of consistent labelling using genetic search. The work builds on a recent empirical study of graph matching where we showed that a Bayesian consistency measure could be e$ciently optimised using a hybrid genetic search procedure which incorporated a hill-climbing step. In the present study we return to the algorithm and provide some the...

Parameter tuning is an important step in automatic fuzzy model identification from sample data. It aims at the determination of quasi-optimal parameter values for fuzzy inference systems using an adequate search technique. In this paper, we introduce a new hybrid search algorithm that uses a variant of the cross-entropy (CE) method for global search purposes and a hill climbing type approach to...